Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197899,
        author = {MIHIRKUMAR ASHOKBHAI MISHRA},
        title = {Applications of Machine Learning in Agriculture: A Comprehensive Review of Methods, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11229-11235},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197899},
        abstract = {Agriculture plays a pivotal role in ensuring global food security, yet it faces challenges such as climate change, resource scarcity, and population growth. Machine Learning (ML) has emerged as a transformative technology for addressing these challenges by enabling data-driven decision-making in agriculture. This review paper synthesizes the current state-of-the-art applications of ML in agriculture, including crop yield prediction, plant disease detection, irrigation management, soil fertility assessment, weed and pest control, and supply chain optimization. We provide a systematic review of literature published between 2018 and 2025, emphasizing methodologies, datasets, and performance outcomes. The review highlights challenges including lack of annotated datasets, high computational costs, and limited adoption among small-scale farmers. Finally, we outline future directions such as explainable AI, federated learning, integration with IoT, and climate-resilient models. This paper contributes a comprehensive overview and critical insights to guide researchers, policymakers, and practitioners towards sustainable and technology-driven agriculture.},
        keywords = {Agriculture, Crop Yield Prediction, Deep Learning, Farming, Machine Learning, Precision Farming, Smart},
        month = {April},
        }